Tech3q’s Post

NLP involves converting text into numerical representations using mathematical formulas. For example, in sentiment analysis, a basic formula could be: Sentiment Score=Positive Words Count−Negative Words Count This simple equation calculates sentiment by subtracting the count of negative words from the count of positive words in a given text, providing a basic numerical representation of sentiment.

📰 Fake News Prediction System Completed! 📰 In my latest project, I developed a Fake News Prediction System using NLP techniques to process the dataset and build an efficient model with Logistic Regression. 🔍 Dataset Details: Contains attributes like id, title, author, text, and label (1 for unreliable, 0 for reliable). 🚀 What I Did: 1. Text Preprocessing with NLP: Applied techniques like stemming and stopword removal to clean and process the text data. 2. TF-IDF Vectorization: Converted the news text into numerical features for better model performance. 3. Model: Built a Logistic Regression model. 4. Achieved an accuracy of 98.66% on the training set and 97.91% on the testing set! 🔗 Check out the full project here: https://lnkd.in/d57Zti8F

Machine Learning Quiz: 𝑩𝒂𝒈 𝒐𝒇 𝑾𝒐𝒓𝒅𝒔 𝑴𝒐𝒅𝒆𝒍 (𝑬𝒂𝒔𝒚) Quiz link: https://lnkd.in/gkZQuM3x Bag of Words model is a foundational technique in NLP for textual analysis for tasks such as document classification, sentiment analysis, and topic modeling. It is simple, straightforward and computationally efficient technique that often serves as a strong baseline for text representation in numerical form. Take this 𝓔𝓪𝓼𝔂 quiz on ’Bag of Words’ model to test your knowledge on the topic. 𝐖𝐡𝐲 𝐀𝐈𝐌𝐋.𝐜𝐨𝐦 𝐐𝐮𝐢𝐳𝐳𝐞𝐬 𝐚𝐫𝐞 𝐀𝐰𝐞𝐬𝐨𝐦𝐞: 👉 Get your quiz scores in real time 📚 In-depth answer explanations to help understand the concepts clearly 🎉 Completely FREE! Learning resources for this quiz: https://lnkd.in/gg8kE3iT ✅ 𝑱𝒐𝒊𝒏 𝑨𝑰𝑴𝑳.𝒄𝒐𝒎 𝒕𝒐 𝒕𝒂𝒌𝒆 𝒎𝒐𝒓𝒆 𝒔𝒖𝒄𝒉 𝒒𝒖𝒊𝒛𝒛𝒆𝒔. 𝑾𝒆 𝒉𝒂𝒗𝒆 60+ 𝒒𝒖𝒊𝒛𝒛𝒆𝒔 𝒄𝒐𝒗𝒆𝒓𝒊𝒏𝒈 𝒂 𝒘𝒊𝒅𝒆 𝒓𝒂𝒏𝒈𝒆 𝒐𝒇 𝑴𝒂𝒄𝒉𝒊𝒏𝒆 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 𝒕𝒐𝒑𝒊𝒄𝒔.

🌟 Day 65/100 🌟 🚀 Challenge: Check If a Word Occurs as a Prefix of Any Word in a Sentence 🔍 Problem: Given a sentence and a word, determine if the word is a prefix of any word in the sentence. If yes, return the 1-based index of the first occurrence. If not, return -1. 💡 ⚙️ Why It Matters: Understanding prefixes helps in text processing tasks like autocomplete, search optimizations, and NLP. It's a great warm-up for string manipulation challenges! 💡 Key Takeaway: Breaking problems into simple steps and efficiently iterating through sentences can work wonders. Let’s dive deeper into string processing today! 🧩 📜 Code, Solution, and Explanation in the comments below! 🚀 Let me know if you'd solve this differently or optimize further! 💬 #100DaysOfDSA #ProblemSolving #StringsInFocus #CodingChallenge

When you call nlp on a text, spaCy first tokenizes the text to produce a Doc object. The Doc is then processed in several different steps – this is also referred to as the processing pipeline. The pipeline used by the trained pipelines typically include a tagger, a lemmatizer, a parser and an entity recognizer. Each pipeline component returns the processed Doc, which is then passed on to the next component.

Word Embeddings and Semantic Similarity Word embeddings are a fundamental concept in NLP, representing words as vectors in a continuous vector space. This allows the model to capture semantic similarities between words. Key Points: Vector Representation: Words are represented as dense vectors, capturing semantic relationships. Techniques: Popular techniques include Word2Vec, GloVe, and FastText. Applications: Used in tasks like clustering, classification, and information retrieval. By understanding word embeddings, we can better appreciate how NLP models grasp the meaning and relationships between words.

Which is quicker for processing a ~10GB text dataset locally: using IbisData with Duckdb or Polars Dataframe? There are numerous methods for processing text data, and while Pandas remains a convenient option, it's time to consider alternatives. 😇 "processing": Complex String Manipulations and Multi-level transformations, fo NLP data processing.

End-to-End NLP Project with Hugging Face, FastAPI, and Docker: This tutorial explains how to build a containerized sentiment analysis API using Hugging Face, FastAPI and Docker Continue reading on Towards Data Science » #MachineLearning #ArtificialIntelligence #DataScience

#Assignment_Alert! Here is the assignment from the NLP playlist CampusX Lecture 3 on Text Preprocessing. #What_I_Learn I have learned about text preprocessing during this lecture, As well as how to retrieve data from APIs and store it in a Data Frame for assignments. Thank you, Nitish Singh Sir, for providing valuable content and knowledge. #Assignment_Task: 1. Retrieve data from the specified APIs. 2. Store the retrieved data into a Data Frame. 3. Perform basic text preprocessing, including:   - Improving the wording of the text.   - Adding additional words to enhance the content. #dataanalytics #deeplearning